Ranking with Adaptive Neighbors
This addresses ranking sensitivity in retrieval tasks for domains like web search and visual retrieval, but it is incremental as it builds on graph-based approaches.
The paper tackles the problem of retrieving similar objects in large-scale databases by proposing a ranking algorithm that learns both data affinity and ranking scores simultaneously, and evaluations show it outperforms existing methods.
Retrieving the most similar objects in a large-scale database for a given query is a fundamental building block in many application domains, ranging from web searches, visual, cross media, and document retrievals. State-of-the-art approaches have mainly focused on capturing the underlying geometry of the data manifolds. Graph-based approaches, in particular, define various diffusion processes on weighted data graphs. Despite success, these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix. In this study, we propose a new ranking algorithm that simultaneously learns the data affinity matrix and the ranking scores. The proposed optimization formulation assigns adaptive neighbors to each point in the data based on the local connectivity, and the smoothness constraint assigns similar ranking scores to similar data points. We develop a novel and efficient algorithm to solve the optimization problem. Evaluations using synthetic and real datasets suggest that the proposed algorithm can outperform the existing methods.